Medicaid Value-Based Payments and Health Care Use for Patients With Mental Illness

This cohort study investigates the association of New York state’s Medicaid value-based payment reform with utilization patterns in patients with mental illness.


Mental Illness Diagnoses Definitions:
a. Defining the mentally ill population: We use the Clinical Classification Software (CCS) and ICD-9 codes to define the depression, bipolar, and schizophrenia populations.We used the following CCS codes for initial population categorization: Depression (657), Bipolar disorder (657), and Schizophrenia (659).Since CCS code 657 represents Mood disorders, which encompasses both depression and bipolar disorders, we further distinguish between these diagnostic populations using ICD-9 codes. [6]. health specific admissions were counted if they had a primary diagnosis with the following CCS code [7] : 650, 651, 652, 655, 656, 657, 658, 659, 662, 663, 670.

Additional information on key variables:
We fixed VBP at the first year of implementation for several reasons.First, a majority of practices begin VBP in this year (~70%) and the attrition rate of practices out of VBP is very low (<1%).Second, practices who join in later years have substantially less time to plan and execute VBP funded delivery reform projects and are, therefore, a less representative group for the effects of VBP.Third, by fixing treatment at baseline, we do not introduce additional selection bias of practices joining VBP after witnessing the benefits of the program.In Supplemental Section 8, we conduct sensitivity analyses with time-varying VBP indicators and samples to assess whether coding late-VBP entrants as non-VBP biases our main estimates.

Regression model specifications
Our main empirical strategy was a difference-in-differences (DiD) analysis.We stratify the analysis by psychiatric diagnosis and estimate the following model:  !"# =  $ +  %  #  " +  " +  !+  !"# Where  !#" is the expected outcome for an individual patient i in a given year t attributed to practice k.  " is year fixed effects, and  ! is patient-level fixed effects.The patient and year level fixedeffects control for time-invariant observable and unobservable factors at the patient and year level, including the individual indicators for the pre-VBP period and attribution to VBP.  #  " is the interaction between the indicator for a patient attributed to a VBP practice and the indicator being in the post-VBP period.The coefficient  % is the coefficient of interest, and estimates the adjusted mean differential change in utilization due to a patient's practice participation in VBP incentives compared to the non-VBP group.We used a linear regression and cluster standard errors at the practice level (the level of treatment assignment).
For reference, we also include an unadjusted model that lacks patient and year level fixed effects in ETables 2 and 3.The unadjusted model is specified by the following model: Where  !#" is the expected outcome for an individual patient i in a given year t attributed to provider k.  # indicates if a patient is attributed to VBP participating practice, and  " is an indicator for being in the post-VBP implementation period.The interaction term between  #  represents the differential change in outcomes for a patient whose outpatient practice participates in VBP reform compared to the non-VBP group in the post-VBP period.

Construction of utilization outcomes in the Medicaid claims data
Outcomes are the number of visits per patient-year.Visits are identified as an aggregation of claims per patient-NPI-date so that all claims associated with one visit are counted as a single visit.To categorize visits, we use a Category of Service variable derived from an algorithm of NPI specialty codes, place of service variables, procedure codes, and rate codes to classify claims as belonging to a service (primary care vs mental health, etc).

Comparing patient characteristics between analytic sample and those excluded due to insufficient enrollment.
By restricting the analytic population to Medicaid patients with 12 months of continuous enrollment, we may be selecting a particular patient population that is different from the general Medicaid population and whose underlying characteristics confound our measured associations.We compared patient-level characteristics of the analytic population and those excluded due to insufficient enrollment to evaluate potential underlying differences.The analytic population has 12 months of enrollment starting in July 1 of each year.The insufficient enrollment population are all patients who do not meet this criterion in a given year.Overall, the characteristics between the analytic population and the population excluded because of insufficient enrollment are similar across key characteristics.There are 1,486,229 number of patient-years excluded throughout the study period.Supplemental ETable 2 compares characteristics between analytic and excluded populations.The analytic and excluded populations are comparable for average age (40.8 vs 40.5 years, respectively), sex (64% female vs 60% female, respectively), and residence in NYC (50% vs 50%, respectively).There is relative balance across ethnicity between analytic population and excluded populations [non-Hispanic White: 39% v 44%; Black: 20% vs 17%; Asian: 4.0% vs 4.0%; Native American: 4% vs 5%; non-white Hispanic: 18% vs 14%].The analytic population vs excluded population is slightly more likely to be attributed to the VBP group (80% vs 75%), has a slightly higher average Charlson co-morbidity index (2.1 vs 1.6), and slightly higher managed care enrollment (95.3% vs 90.9%).Our analytic population has higher total enrollment in the study period (70.1 months vs 44.9 months), which aligns with the exclusion criterion (insufficient enrollment).

Robustness Checks
All-cause hospitalizations and emergency room visits Patients with mental illness have a higher burden of physical chronic illnesses, and tend to have higher proportion of adverse health events such as hospitalizations and emergency room visits compared to the general population.Previous studies on mental health interventions use all-cause hospitalizations and emergency room visits as proxies for the quality-of-care mental health patients receive related to their chronic disease management. [8,9] n addition to mental-health related hospitalizations and emergency room visits, we also examined VBPs impact on all-cause admissions (Supplemental ETable 3).

Time-varying VBP indicator
We explore how time-varying NPI participation in VBP may influence the association VBP has with our outcomes.First, we construct a VBP indicator which is 1 for every year an NPI is in our VBP dataset and zero when they are absent.8.1% of patient-years become treated if we allow their attributed practice to enter VBP after the baseline year.We then performed a difference-in-differences analysis © 2023 Lewis A et al.JAMA Health Forum.using the adjusted model described in Supplemental eSection 5 using this time-varying VBP indicator.The interaction term between the VBP indicator (time-varied throughout the post-period) and post-VBP indicator will be the average, differential effect of VBP on outcomes after VBP is implemented.Results are presented in Supplemental eTable 4 and Supplemental eTable 5. Overall, we find that our results remain similar in both magnitude, direction, and precision to our main specification.Next, we exclude patients whose attributed practice is non-VBP at baseline but then enters VBP in the post-period.This allows us to measure if and to what degree our main specification estimates could be biased due to misclassification of late-entering VBP practices as non-VBPs.On the new analytic population of we perform a difference-in-differences using the adjusted model described in Supplemental Section 5 with the results in Supplemental eTable 6 and Supplemental eTable 7. Overall, we find that our results remain similar in both magnitude, direction, and precision to our main specification.The combined results of the time-varying VBP indictor analyses suggest that changes throughout the post-period in practice-level VBP status do not significantly drive our main estimations.

Inclusion of pre-VBP outcome trends into model
In the results of Figure 2, we detect small but significant pre-trends for the primary care outcome.To test the parallel trends assumption, we estimated our main model with an additional covariate to test for differences between VBP and non-VBP groups in pre-intervention trends in outcomes.This term was specified as an interaction between  # and a linear time trend (indicator for Pre-VBP period), which estimates the differential yearly change in the outcome prior to VBP implementation and after VBP implementation in VBP patients versus the non-VBP patients.Accepting the null hypothesis would be consistent with the parallel trends assumption.Our results are in Supplemental eTable 8 and Supplemental eTable 9.The results for our primary outcomes remain relatively similar.The estimates for the linear pre-trend, which estimates if a differential change during the pre-VBP period is detected in the VBP group relative to the non-VBP, is significant for the primary care outcome trend for bipolar and schizophrenia VBP patients.No other outcomes demonstrate a significant differential trend during the pre-VBP period.Our estimates measuring the differential change in primary care visits for VBP patients after VBP reform -the estimate of the association between VBP and primary care visits-demonstrate a statistically significant reduction in schizophrenia patients (-1.49, [-2.27, -0.27]), similar to our main findings, indicating pre-VBP outcome trends do not drive our results.For the remaining outcomes, mental health visits are significantly associated with VBP for depression and bipolar populations (Depression: 0.96, [0.55,1.38];Bipolar: 1.06, [0.24,1.8]).This is similar to our main results.Emergency room visits remain statistically significantly associated with VBP for depression and bipolar patients, but not schizophrenia patients (Depression: -0.09, [-0.15,-0.03;Bipolar: -0.13, [-0.26,-0.01];Schizophrenia: -0.18, [-0.35,0.00]).The effects for hospitalizations are attenuated with the inclusion of pre-VBP outcome trends, despite non-significant differential pre-VBP outcome trends.

Analysis of outcomes at the six-month period level
In order to assess the outcome trends and changes with more granularity, we visualized the outcome trends for the VBP and non-VBP patient groups at the six-month period level.This allows four periods pre-VBP and eight periods post-VBP.eFigure 1 depicts these outcome trends, stratified by diagnostic group.We find that our outcome trends appear to follow a generally parallel pattern across outcomes and diagnostic populations with the exception of mental health hospitalizations for patients with schizophrenia and bipolar disorder.The smaller patient population, rare event of mental health hospitalizations, and more granular time period likely leads to higher levels of noise.To assess whether, at the six-month interval, pre-trend deviations bias results, we conduct two difference-in-differences at the six-month period level: 1) estimating our main adjusted model (outlined in eSection 5) to measure the differential change in outcomes for VBP patients after VBP at the six-month period level and 2) estimating our model outlined in "Inclusion of pre-VBP outcome trends into model" that incorporated pretrends into our model.The addition of pre-trends as a covariate will control for pre-trend deviations between the VBP and non-VBP group.Results for our main model specifications at the six-month level are in eTable 10 and the results for the analysis with included pre-trends is in eTable 11.We find that for outpatient utilization, the direction and significance at the six-month level is similar to that of the yearlevel across diagnostic group.At the six-month level, we find that mental health hospitalizations results remain similar to the year level in significance and direction, but we find the effects on mental health ED visits attenuated.

Population Compositional changes: Patient characteristics as outcomes
The composition of patient characteristics between VBP and non-VBP patients could change over time.This is a concern if VBP or non-VBP providers are selecting patients according to their participation in VBP after implementation.To test whether patient characteristics change differentially for the VBP and non-VBP groups before and after VBP, we use age, sex, and patient co-morbidity as outcomes.These characteristics should be unrelated to VBP implementation and should not differentially change over time.Accepting the null hypothesis would represent a failure to detect of selection, along these characteristics.Patient age, sex, and Charlson co-morbidity score were set as dependent variables.Charlson comorbidity is fixed at the pre-VBP score.Since these are fixed characteristics, patient fixed effects would produce total collinearity.Instead, we specify the following model: Where  !#" is a patient characteristic (age, sex, Charlson Co-morbidty) for an individual patient i in a given year t attributed to provider k.  # indicates if a patient is attributed to VBP participating provider,  " is an indicator for being in the post-intervention period,  " is year fixed effects,  # is NPI fixed-effects, and  ! is the patient characteristics that are not the dependent variable (e.g.inclusion of age and sex when Charlson co-morbidity score is the dependent variable).The main effect of interest, the interaction between  #  " , is interpreted as the differential change in patient characteristic for the VBP vs non-VBP group in that year relative to the first year of our study period (July 1, 2013-July 1, 2014 and first pre-VBP year).Supplemental ETable 12 reports the estimates from this analysis.
Overall, we do not detect differential change in age, sex, and Charlson co-morbidity scores for VBP vs non-VBP groups throughout the course of the study.We do detect a small, statistically significant differential decrease in age for the VBP group in the post-VBP for the population with depression relative to the first year of the study period.We also detect a small relative increase in Charlson co-morbidity scores for the bipolar population in early years that is no longer statistically significant in the later study period years.This suggest there may be compositional changes in the VBP group over time.We test the extent to which these changes may drive our results by reducing our analytic sample to a balance panel with continuously enrolled patients (Supplemental Section 8: Balanced Panel)

Log-linear regression
Patients with mental illness have a wide range of utilization and studies on utilization can be biased due to extreme values or outliers.To mitigate the effects of utilization outliers on our estimated effect, we took the natural logarithm of each outcome and performed a log-linear model.We used these transformed outcomes as the dependent variables using the adjusted patient and year level fixed effects described in Supplemental Section 3.For observations where the outcome is zero, the zero was replaced with 0.1.The reported estimates and confidence intervals are transformed (exponentiated the coefficient and subtract 1) to reflect the relative percent change in outcomes.Supplemental eTable 13 and Supplemental eTable 14 report the estimates from this analysis.We find that our primary outcomes of mental health visits and primary care visits remain robust.In fact, we find that VBP is statistically significantly associated with reductions in the percent of primary care visits for depression and bipolar patients (Depression: -3.0%; [-5.0,-1.0];Bipolar: -5.0%; [-9.0,-2.0])and VBP is statistically significantly associated with mental health visits for schizophrenia patients (3.0%; [0.3, 7.0]).This indicates outliers may have suppressed the effects in these outcomes.For secondary outcomes, we find that the associations are directionally similar to the main findings but the precision of the effects differ and the significance is attenuated.

Two-part model: Association of VBP on the change in any utilization per outcome and the change in utilization intensity per outcome
The main analysis estimates reflect aggregated utilization, but do not discern utilization changes related to changes along extensive margins (no utilization to any utilization, or vice versa) or the intensive margins (utilizers changing the intensity of their utilization).To distinguish the type of utilization changes after VBP reform, we conduct a two-part model.The model will first determine the likelihood of changing from zero to any utilization, then, conditional on non-zero utilization determine the degree of change in utilization.We performed a logistic regression which estimates whether VBP is associated with changes in a binary outcome, where zero represents no utilization in that outcome and one represents any utilization in that outcome.Next, conditional on non-zero utilization, we estimated a patient and year level fixed effects linear regression to assess the association between VBP and outcomes.The reported estimates of the logistic regression are the average marginal effects of the association of VBP in the post-VBP period and can be interpreted as the percent change in the likelihood of having any utilization.Supplemental ETable 15 and Supplemental ETable 16 report the estimates from these analyses.For depression and bipolar patients, we find that VBP is associated with an increase in the likelihood of any mental health visits (Depression: 31.0%,[20.4,42.2]; Bipolar: 20%, [2.3, 37.7]) and, conditional on any mental health visits, an increase in the number of visits (Depression: 1.57 visits, [0.57, 2.57]; Bipolar: 1.74 visits, [0.27, 3.21]).

Balanced Panel: Continuously enrolled population
Medicaid patients have significant enrollment churn.Our main analysis allows patient-year observations that meet the enrollment requirements to be included in the analysis, while excluding patient-year observations that fail to meet 12 months enrollment in that year.A potential threat to the analysis is that the patients disenrolling in Medicaid after VBP implementation are different from those disenrolled before VBP implementation, differential to VBP attribution in ways that confounds the estimated VBP association with our outcomes.In Supplemental ETable 12, we find that for VBP depression patients, there is a differential decrease in the age over time.This suggests the composition in the VBP depression group, along the dimension of age, could change differentially and confound our results.By restricting our analysis to a balanced panel, which requires beneficiaries to be enrolled for 12 months in every year of our study, we no longer have patients dis-enrolling in the study period.Therefore, the population characteristics are static in the study period.We restrict the population to patients who are continuously enrolled from July, 1 2013-July 1, 2019.On this balanced panel, we perform the same model described in Supplemental Section 5a.
3,384,912 (45.1%) patients were excluded from the balanced panel due to insufficient enrollment (not being continuously enrolled).Supplemental eTable 17 and Supplemental eTable 18 report the estimates from this analysis.We find consistent results with our main specification for behavioral health and primary care outcomes.VBP is statistically significantly associated with increased behavioral health visits in depression and bipolar patients and with reductions in primary care for schizophrenia patients.For secondary outcomes, the associations with reductions in emergency room visits remain statistically significant and are of a similar magnitude to the main analysis (eTable 3).The VBP associations for hospitalization outcomes are a similar direction but the precision is attenuated.Overall, the restriction to a balanced panel demonstrates that differential composition changes in our population are not a driver of our findings.

Reduced analytic Population to patients with Consistent Exposure Status throughout post-VBP period
Medicaid patients have fragmented care, often seeing more than one provider or practice.This may interrupt consistent exposure to VBP reform, since patient may change providers or practices over time who may or may not be participating in DSRIP.To test how continuity to VBP exposure impacts our estimated associations, we restrict our sample to patients who, upon reattribution in the post-VBP period, do not switch treatment assignment from baseline their baseline status (i.e.VBP or non-VBP throughout the study period).Reattribution in the post-VBP period follows the same procedure outlined in Supplemental eSection 2 for the post-VBP years of July 1, 2016-July 1, 2017 and July 1, 2018-July 1, 2019.If the patient's attributed practice in both post-VBP years is that same group as their baseline practice (either in VBP or not in VBP), then we include this patient into the new population for this analysis.This procedure allows patients to be included in the new population even if their attributed practice changes, as long as the exposure status (VBP or non-VBP) is constant.
The model is the same as the one estimated in Supplemental Section 5. Supplemental ETable 19 and Supplemental ETable 20 report the estimated associations.We find that VBP's association with behavioral health visits are similar in statistical significance and magnitude as our main results.In contrast, for each diagnosis, we find that the association of VBP with primary care is statistically significant and in the inverse direction from the main results.Now VBP is associated with an increase in primary care visits rather than a decrease (Depression: 1.58, [1.0, 2.1]; Bipolar: 2 .62[1.7, 3.5]; Schizophrenia: 1.79 [0.9, 2.7]).Our secondary outcomes-hospitalizations and emergency room visits-are not statistically significant or are very small (e.g., Depression, Mental Health Hospitalizations: -0.01, [0.0, 0.0]).For this higher-continuity population, the inverse direction of the association between VBP and primary care suggest that this utilization outcome may require longer-term exposure to VBP in order to translate into substantial changes.

Quality outcomes after VBP reform
Direct measures of quality improvements, such as depression screening information, are not available in administrative data. [10]However, ED visits and hospitalizations are highly correlated with healthcare quality and have been used has valid proxies in claims data. [11,12] ental health related emergency room and hospitalizations are considered important measures of quality for the patient populations with mental illness, specifically.To further illustrate quality changes in DSRIP, we have included hospital readmissions and preventative hospitalizations as additional outcomes.Both of these outcomes were specific metrics used in pay-for-performance evaluations in DSRIP. [13]Readmissions were calculated as another inpatient hospital admission (all-cause) within 30 days of an inpatient admission (all-cause).Preventable hospitalizations were defined as any admission with a primary diagnosis that met the requirements for preventable admission in AHRQ's PQI #92 Chronic Disease Indicator.
Supplement eTable 21 demonstrates the results of the difference-in-difference analysis for 30-day readmissions and preventable hospitalizations.Overall, we find no significant changes in these outcomes associated with VBP reform.This may suggest that the VBP reform for mental health patients may have had the most impact directly on mental healthcare rather than more general health quality.This is noted by participating health systems and clinicians, which stated patients with mental illness were the population who most benefited from reform and most noted mental healthcare was positively transformed. [14]2023 Lewis A et al.JAMA Health Forum.

Combined outpatient utilization after VBP reform
In our main analysis, we find VBP is associated with an increase in mental health visits for depression and bipolar patients and a reduction in primary care for schizophrenia patients.We investigate whether there is a net change in overall outpatient visits to investigate whether there is a net change in outpatient usage, by combining primary care and mental health visits together.Supplemental ETable 22 reports the estimates from this analysis.Overall, we are finding that there is not a significant net change in outpatient utilization, suggesting that there is a redistribution of the outpatient services.
In administrative claims data, it is difficult to measure exact services delivered due to coding inconsistencies with mental healthcare, often biasing toward under-reporting. [15,16] herefore, this outcome likely underestimates the actual prevalence of mental health services provided during primary care visits.Supplemental ETable 23 reports the estimates from this analysis.We find that, compared to control patients, VBP patients with depression and bipolar disorder have a relative increase in MHPC visits after reform is implemented [Depression: 0.02 visits (0.01, 0.04); Bipolar: 0.02, (0.0031, 0.04)], while patients with schizophrenia have no significant difference.Although preliminary, this analysis does not provide evidence that VBP is differentially impeding access to mental healthcare in primary care settings.

Multiple Comparisons Correction
Because we use multiple diagnostic populations and utilization outcomes, our results could be at risk of detecting a significant result that is not true (Type 1 error).To account for this, we corrected our adjusted regression results by applying the false discovery rate procedure (Benjamini and Yekutieli method), [17] which allows for correlation across tests.This procedure adjusts the p-values for each estimate and calculates a new significance level to reject the null hypothesis.
Supplemental eTable 24 depicts the results from the multiple testing analysis with corrected p-values and the adjusted significance value of 0.021, meaning if the adjusted p-values are below 0.021 then the null hypothesis (VBP is not associated with a change in outcome) can be rejected.We find that for patients with depression, VBP was significantly associated with increased mental health visits and decreased mental health hospitalizations and mental health ED.For patients with bipolar disorder, VBP are associated with increased mental health visits.Outpatient utilization and mental health ED and hospitalizations changes were not significantly associated with VBP for patients with schizophrenia

Comparison of effects for patients in New York City vs Rest of New York state
There was significant geographic heterogeneity on the focus of VBP projects and target populations for NY DSRIP and there is significant variation in the Medicaid populations across the state.The largest contrast is between New York City (NYC) versus non-NYC DSRIP programs.NYC's Medicaid © 2023 Lewis A et al.JAMA Health Forum.population generally has higher disease burden, more likely to be non-White, qualify based on SSI, and older than those in the rest of state. [18,19] he NYC DSRIP networks typically had a smaller geographical capture area which had overlap with other DSRIP networks while non-NYC DSRIP networks spanned several counties and sometimes were the only DSRIP networks in the county.NYC DSRIP networks had a larger average number of engaged mental health providers in the network as well (296 vs 148 mental health providers in collaborative care).We aim to assess how the VBP-outcome associations may vary according to these geographical differences.We created an indicator for if a patient's zip code was a NYC zipcode.This zip-code was set at baseline (July 1-2014 -June 30, 2015).We then performed the adjusted patient and year level fixed effects model described in Supplemental Section 5, stratified by patient zipcode.
Supplemental ETable 25 and Supplemental ETable 26 report the results from this analysis.We find that VBP is associated with statistically significant reduction in primary care across diagnoses for non-NYC VBP patients (Depression: -0.91, [-1.2,-0.6];Bipolar: -1.51, [-2.0,-1.0];Schizophrenia: -1.74; [-2.4,-1.1])but not NYC VBP patients.Overall, VBP is statistically significantly associated with mental health increases for both NYC and non-NYC.VBP associated reductions in emergency room visits appear to be driven by non-NYC VBP patients whereas NYC patients have no significant changes in hospitalizations or emergency room visits, except a very small reduction in mental health hospitalizations (-0.01, [0.0,0.0]).

Stratification by co-morbidity burden in schizophrenia patients
We found VBP was associated with significant and large reductions in primary care (Table 2) and ED visits (Supplemental ETable 1a) for schizophrenia patients.This result counters our hypothesis that outpatient utilization (primary care) is inversely related to hospitalization and emergency room utilization.Previous literature has shown that Medicaid schizophrenia patients have different utilization in primary care and emergency room visits based on the severity of co-morbidity burden.Medicaid schizophrenia patients with more co-morbidities have higher utilization, generally, in order to manage complex physical chronic illnesses. [20]Therefore, the VBP associations of reduced primary care and emergency room visits may be happening in different disease-burdened populations.To investigate whether VBP has heterogeneous impact for the schizophrenia population based on co-morbidity burden, we stratify our model (Supplemental eSection 5) by a categorical Charlson Co-Morbidity score.Lower scores represent fewer co-morbidities.Supplemental ETable 27 reports the stratified associations.We find that the means of our outcomes, except for mental health hospitalizations and mental health ED visits, increase as comorbidity score increases.This aligns with the higher utilization driven by physical illness rather than increased severity of mental illness.We find that VBP is statistically significantly associated with primary care reduction (-1.31, [-2.1, -0.49])only in patients with the lowest co-morbidity burden.We also find that VBP is only statistically significantly associated with emergency room reductions (-0.56, [-1.06, -0.06]) in patients with the highest co-morbidity burden.This demonstrates that VBP associated primary care reductions are concentrated in patients with the fewest co-morbidities, who likely have less chronic illness management.This may indicate fewer primary care visits is clinically appropriate.For patients with the highest degree of need, we find not changes in primary care but do find VBP associated emergency room reductions, which was a core aim of NY DSRIP program.
© 2023 Lewis A et al.JAMA Health Forum.

eTable 13 .
Log-transformed primary outcomes: Differential change in outcomes after VBP for VBP attributed A et al.JAMA Health Forum.

eTable 14 .eTable 15 .
Log-transformed secondary outcomes: Differential change in outcomes after VBP for VBP attributed patients Relative % change in outcomes for VBP patients after Two Part Model: Differential change in primary outcomes after VBP for VBP patients

eTable 18 .
Balanced Panel secondary outcomes: Differential change after VBP for VBP attributed A et al.JAMA Health Forum.

eTable 26 .
NYC vs non-NYC: Differential change in secondary outcomes after VBP for VBP patients NYC population: Change in outcomes trends VBP patients post-VBP reform non-NYC population: Change in outcomes trends for VBP patients post-[-0.1,0.0][-0.1,0.0]© 2023 Lewis A et al.JAMA Health Forum.

eFigure. The Mean Number of Visits of six-month period for VBP and non-VBP groups, stratified by diagnosis.
Jul 14 Jul 15 Jul 16 Jul 17 July 18 Jul 13 Jul 14 Jul 15 Jul 16 Jul 17 July 18 Jul 13 Jul 14 Jul 15 Jul 16 Jul 17 July 18The y-axis represents the means patient utilization by VBP status throughout our study period (x-axis).The red vertical line indicates VBP implementation on July 1, 2015."MH" refers to "mental health".6. Restricted to patients whose practice enters VBP in the First VBP year: Differential c hange in primary outc omes after VBP for baseline VBP patients 7. Restricted to patients whose practice enters VBP in the First VBP year: Differential change in secondary outcomes after VBP for baseline VBP patients 8. Incorporation of Pre-VBP outcome trends: Differential change in primary outcomes after VBP for VBP patients 9. Incorporation of Linear Pre-VBP outcome trends: Differential change in secondary outcomes after VBP for VBP patients eTable 11.Incorporation of Pre-VBP outcome trends the six-month period level: Differential change in primary outcomes after VBP for VBP patients Differential change in outcome trend for VBP patients in the post-VBP period © 2023 Lewis A et al.JAMA Health Forum.a.Total of unique NPIs in analytic samples b.Average number of unique patients per NPI c. Percent of NPI-type that is safety-net as indicated on NY DOH website © 2023 Lewis A et al.JAMA Health Forum.©2023LewisAet al.JAMA Health Forum.©2023LewisAet al.JAMA Health Forum.©2023LewisAet al.JAMA Health Forum.eTable©2023LewisA et al.JAMA Health Forum.eTable©2023Lewis A et al.JAMA Health Forum.eTable©2023 Lewis A et al.JAMA Health Forum.eTable© 2023 Lewis A et al.JAMA Health Forum.© 2023 Lewis A et al.JAMA Health Forum.© 2023 Lewis A et al.JAMA Health Forum.
16. Two Part Model: Differential change in secondary outcomes after VBP for VBP patients © 2023 Lewis A et al.JAMA Health Forum.eTable© 2023 Lewis A et al.JAMA Health Forum.© 2023 Lewis A et al.JAMA Health Forum.
20.Restricted patient population whose VBP exposure status is consistent throughout study period: Differential change in secondary outcomes after VBP for VBP attributed patients 23.Differential change in Mental Health Primary Care (MHPC) visits after VBP for VBP attributed patients 24.Multiple Comparison Adjustment for primary and secondary outcomes 25.NYC vs non-NYC: Differential change in primary outcomes after VBP for VBP patients © 2023 Lewis A et al.JAMA Health Forum.eTable © 2023 Lewis A et al.JAMA Health Forum.© 2023 Lewis A et al.JAMA Health Forum.© 2023 Lewis A et al.JAMA Health Forum.eTable © 2023 Lewis A et al.JAMA Health Forum.eTable © 2023 Lewis A et al.JAMA Health Forum.eTable © 2023 Lewis A et al.JAMA Health Forum.
eTable 27.Differential change in outcomes after VBP for Schizophrenia VBP patients stratified by Charlson Co-Morbidity Score Categorical Charlson Co-morbidity Score